Two-Sample Testing for Tail Copulas with an Application to Equity Indices
提出一种新的两样本假设检验方法，用于检验双变量数据尾部连接函数的相等性，通过蒙特卡洛模拟验证其有限样本表现，并应用于全球金融危机前后股票指数日负对数收益率的尾部依赖比较。
A novel, general two-sample hypothesis testing procedure is established for testing the equality of tail copulas associated with bivariate data. More precisely, using a martingale transformation of a natural two-sample tail copula process, a test process is constructed, which is shown to converge in distribution to a standard Wiener process. Hence, from this test process a myriad of asymptotically distribution-free two-sample tests can be obtained. The good finite-sample behavior of our procedure is demonstrated through Monte Carlo simulations. Using the new testing procedure, no evidence of a difference in the respective tail copulas is found for pairs of negative daily log-returns of equity indices during and after the global financial crisis.